Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization

Deep neural networks (DNNs) can be useful within the marine robotics field, but their utility value is restricted by their black-box nature. Explainable artificial intelligence methods attempt to understand how such black-boxes make their decisions. In this work, linear model trees (LMTs) are used t...

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Detalles Bibliográficos
Autores principales:	Vilde B. Gjærum, Inga Strümke, Ole Andreas Alsos, Anastasios M. Lekkas
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep reinforcement learning autonomous surface vessel explainable artificial intelligence linear model trees Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/1cef3c3316ff413abd15bb5f091ca66f
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Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization

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